A simplified interface for NLP modeling/solving. More...

#include <optistack.hpp>

Detailed Description

This class offers a view with model description facilities The API is guaranteed to be stable.

Example NLP:

  opti = casadi.Opti();

  x = opti.variable();
  y = opti.variable();

  opti.minimize(  (y-x^2)^2   );
  opti.subject_to( x^2+y^2==1 );
  opti.subject_to(     x+y>=1 );

  opti.solver('ipopt');
  sol = opti.solve();

  sol.value(x)
  sol.value(y)

Example parametric NLP:

  opti = casadi.Opti();

  x = opti.variable(2,1);
  p = opti.parameter();

  opti.minimize(  (p*x(2)-x(1)^2)^2   );
  opti.subject_to( 1<=sum(x)<=2 );

  opti.solver('ipopt');

  opti.set_value(p, 3);
  sol = opti.solve();
  sol.value(x)

  opti.set_value(p, 5);
  sol = opti.solve();
  sol.value(x)

Date: 2017

Author: Joris Gillis, Erik Lambrechts, Joel Andersson

Extra doc: https://github.com/casadi/casadi/wiki/L_16

Definition at line 88 of file optistack.hpp.

Inheritance diagram for casadi::Opti:

[legend]

Public Types
using	internal_base_type = SharedObjectInternal

using	base_type = SharedObject

Public Member Functions
	Opti (const std::string &problem_type="nlp")
	Create Opti Context. More...

MX	variable (casadi_int n=1, casadi_int m=1, const std::string &attribute="full")
	Create a decision variable (symbol) More...

MX	variable (const Sparsity &sp, const std::string &attribute="full")

MX	variable (const MX &symbol, const std::string &attribute="full")

MX	parameter (casadi_int n=1, casadi_int m=1, const std::string &attribute="full")
	Create a parameter (symbol); fixed during optimization. More...

MX	parameter (const Sparsity &sp, const std::string &attribute="full")

MX	parameter (const MX &symbol, const std::string &attribute="full")

void	minimize (const MX &f, double linear_scale=1)
	Set objective. More...

void	subject_to ()
	Clear constraints. More...

void	solver (const std::string &solver, const Dict &plugin_options=Dict(), const Dict &solver_options=Dict())
	Set a solver. More...

Dict	stats () const
	Get statistics. More...

std::string	return_status () const
	Get return status of solver. More...

std::vector< MX >	initial () const
	get assignment expressions for initial values More...

std::vector< MX >	value_variables () const
	get assignment expressions for latest values More...

std::vector< MX >	value_parameters () const

Function	scale_helper (const Function &h) const
	Scale a helper function constructed via opti.x, opti.g, ... More...

MX	dual (const MX &m) const
	get the dual variable More...

casadi_int	nx () const
	Number of (scalarised) decision variables. More...

casadi_int	np () const
	Number of (scalarised) parameters. More...

casadi_int	ng () const
	Number of (scalarised) constraints. More...

MX	x () const
	Get all (scalarised) decision variables as a symbolic column vector. More...

MX	p () const
	Get all (scalarised) parameters as a symbolic column vector. More...

MX	g () const
	Get all (scalarised) constraint expressions as a column vector. More...

MX	f () const
	Get objective expression. More...

MX	lbg () const
	Get all (scalarised) bounds on constraints as a column vector. More...

MX	ubg () const

DM	x_linear_scale () const

DM	x_linear_scale_offset () const

DM	g_linear_scale () const

double	f_linear_scale () const

MX	lam_g () const
	Get all (scalarised) dual variables as a symbolic column vector. More...

OptiAdvanced	debug () const
	Get a copy with advanced functionality. More...

OptiAdvanced	advanced () const
	Get a copy with advanced functionality. More...

Opti	copy () const
	Get a copy of the. More...

void	update_user_dict (const MX &m, const Dict &meta)
	add user data More...

void	update_user_dict (const std::vector< MX > &m, const Dict &meta)

Dict	user_dict (const MX &m) const
	Get user data. More...

std::string	type_name () const
	Readable name of the class. More...

void	disp (std::ostream &stream, bool more=false) const
	Print representation. More...

std::string	get_str (bool more=false) const
	Get string representation. More...

std::string	class_name () const
	Get class name. More...

std::string	debug_repr () const

bool	is_null () const
	Is a null pointer? More...

casadi_int	__hash__ () const
	Returns a number that is unique for a given Node. More...


void	subject_to (const MX &g, const Dict &options=Dict())
	Add constraints. More...

void	subject_to (const std::vector< MX > &g, const Dict &options=Dict())
	Add constraints. More...

void	subject_to (const MX &g, const DM &linear_scale, const Dict &options=Dict())
	Add constraints. More...

void	subject_to (const std::vector< MX > &g, const DM &linear_scale, const Dict &options=Dict())
	Add constraints. More...


void	set_initial (const MX &x, const DM &v)

void	set_initial (const std::vector< MX > &assignments)


void	set_value (const MX &x, const DM &v)
	Set value of parameter. More...

void	set_value (const std::vector< MX > &assignments)
	Set value of parameter. More...


void	set_domain (const MX &x, const std::string &domain)
	Set domain of a decision variable. More...

void	set_linear_scale (const MX &x, const DM &scale, const DM &offset=0)
	Set scale of a decision variable. More...

OptiSol	solve ()
	Crunch the numbers; solve the problem. More...

OptiSol	solve_limited ()
	Crunch the numbers; solve the problem. More...

native_DM	value (const MX &x, const std::vector< MX > &values=std::vector< MX >()) const

native_DM	value (const DM &x, const std::vector< MX > &values=std::vector< MX >()) const
	Set domain of a decision variable. More...

native_DM	value (const SX &x, const std::vector< MX > &values=std::vector< MX >()) const
	Set domain of a decision variable. More...


Function	to_function (const std::string &name, const std::vector< MX > &args, const std::vector< MX > &res, const Dict &opts=Dict())
	Create a CasADi Function from the Opti solver. More...

Function	to_function (const std::string &name, const std::vector< MX > &args, const std::vector< MX > &res, const std::vector< std::string > &name_in, const std::vector< std::string > &name_out, const Dict &opts=Dict())
	Create a CasADi Function from the Opti solver. More...

Function	to_function (const std::string &name, const std::map< std::string, MX > &dict, const std::vector< std::string > &name_in, const std::vector< std::string > &name_out, const Dict &opts=Dict())
	Create a CasADi Function from the Opti solver. More...


void	callback_class (OptiCallback *callback)
	Helper methods for callback() More...

void	callback_class ()
	Helper methods for callback() More...

Static Public Member Functions
static MX	bounded (const MX &lb, const MX &expr, const MX &ub)
	Construct a double inequality. More...

Friends
class	InternalOptiCallback

Member Typedef Documentation

◆ base_type

using casadi::SharedObject::base_type = SharedObject

inherited

Definition at line 103 of file shared_object.hpp.

◆ internal_base_type

using casadi::SharedObject::internal_base_type = SharedObjectInternal

inherited

Definition at line 102 of file shared_object.hpp.

Constructor & Destructor Documentation

◆ Opti()

casadi::Opti::Opti ( const std::string & problem_type = "nlp" )

Parameters

[in] problem_type of optimization 'nlp' or 'conic' (default nlp)

Extra doc: https://github.com/casadi/casadi/wiki/L_17

Member Function Documentation

◆ hash()

casadi_int casadi::GenericShared< SharedObject , SharedObjectInternal >::__hash__

inherited

If the Object does not point to any node, "0" is returned.

Extra doc: https://github.com/casadi/casadi/wiki/L_av

Definition at line 123 of file generic_shared_impl.hpp.

◆ advanced()

OptiAdvanced casadi::Opti::advanced ( ) const

You get access to more methods, but you have no guarantees about API stability

The copy is effectively a deep copy: Updating the state of the copy does not update the original.

Extra doc: https://github.com/casadi/casadi/wiki/L_1m

◆ bounded()

static MX casadi::Opti::bounded	(	const MX &	lb,
		const MX &	expr,
		const MX &	ub
	)

inlinestatic

Constructs: lb(p) <= g(x,p) <= ub(p)

Python prohibits such syntax directly

Extra doc: https://github.com/casadi/casadi/wiki/L_1k

Definition at line 397 of file optistack.hpp.

◆ callback_class() [1/2]

void casadi::Opti::callback_class ( )

Do not use directly.

Extra doc: https://github.com/casadi/casadi/wiki/L_1p

◆ callback_class() [2/2]

void casadi::Opti::callback_class ( OptiCallback * callback )

Do not use directly.

Extra doc: https://github.com/casadi/casadi/wiki/L_1p

◆ class_name()

std::string casadi::SharedObject::class_name ( ) const

inherited

Extra doc: https://github.com/casadi/casadi/wiki/L_au

◆ copy()

Opti casadi::Opti::copy ( ) const

The copy is effectively a deep copy: Updating the state of the copy does not update the original.

Extra doc: https://github.com/casadi/casadi/wiki/L_1n

◆ debug()

OptiAdvanced casadi::Opti::debug ( ) const

You get access to more methods, but you have no guarantees about API stability

The copy is effectively a deep copy: Updating the state of the copy does not update the original.

Extra doc: https://github.com/casadi/casadi/wiki/L_1l

◆ debug_repr()

std::string casadi::GenericShared< SharedObject , SharedObjectInternal >::debug_repr

inherited

Definition at line 112 of file generic_shared_impl.hpp.

◆ disp()

void casadi::Opti::disp	(	std::ostream &	stream,
		bool	more = `false`
	)		const

◆ dual()

MX casadi::Opti::dual ( const MX & m ) const

m must be a constraint expression. The returned value is still a symbolic expression. Use value on it to obtain the numerical value.

Extra doc: https://github.com/casadi/casadi/wiki/L_1h

◆ f()

MX casadi::Opti::f ( ) const

Extra doc: https://github.com/casadi/casadi/wiki/L_26e

◆ f_linear_scale()

double casadi::Opti::f_linear_scale ( ) const

◆ g()

MX casadi::Opti::g ( ) const

Extra doc: https://github.com/casadi/casadi/wiki/L_26d

◆ g_linear_scale()

DM casadi::Opti::g_linear_scale ( ) const

◆ get_str()

std::string casadi::Opti::get_str ( bool more = false ) const

◆ initial()

std::vector<MX> casadi::Opti::initial ( ) const

Extra doc: https://github.com/casadi/casadi/wiki/L_266

◆ is_null()

bool casadi::GenericShared< SharedObject , SharedObjectInternal >::is_null

inherited

Definition at line 116 of file generic_shared_impl.hpp.

◆ lam_g()

MX casadi::Opti::lam_g ( ) const

Useful for obtaining the Lagrange Hessian:

* sol.value(hessian(opti.f+opti.lam_g'*opti.g,opti.x)) % MATLAB
* sol.value(hessian(opti.f+dot(opti.lam_g,opti.g),opti.x)[0]) # Python
*

Extra doc: https://github.com/casadi/casadi/wiki/L_1i

◆ lbg()

MX casadi::Opti::lbg ( ) const

Extra doc: https://github.com/casadi/casadi/wiki/L_26f

◆ minimize()

void casadi::Opti::minimize	(	const MX &	f,
		double	linear_scale = `1`
	)

Objective must be a scalar. Default objective: 0 When method is called multiple times, the last call takes effect

Extra doc: https://github.com/casadi/casadi/wiki/L_1a

◆ ng()

casadi_int casadi::Opti::ng ( ) const

Extra doc: https://github.com/casadi/casadi/wiki/L_26a

◆ np()

casadi_int casadi::Opti::np ( ) const

Extra doc: https://github.com/casadi/casadi/wiki/L_269

◆ nx()

casadi_int casadi::Opti::nx ( ) const

Extra doc: https://github.com/casadi/casadi/wiki/L_268

◆ p()

MX casadi::Opti::p ( ) const

Extra doc: https://github.com/casadi/casadi/wiki/L_26c

◆ parameter() [1/3]

MX casadi::Opti::parameter	(	casadi_int	n = `1`,
		casadi_int	m = `1`,
		const std::string &	attribute = `"full"`
	)

The order of creation does not matter. It is not required for parameter to actualy appear in the optimization problem. Parameters that do appear, must be given a value before the problem can be solved.

Parameters

[in]	n	number of rows (default 1)
[in]	m	number of columnss (default 1)
[in]	attribute	'full' (default) or 'symmetric'

Extra doc: https://github.com/casadi/casadi/wiki/L_19

◆ parameter() [2/3]

MX casadi::Opti::parameter	(	const MX &	symbol,
		const std::string &	attribute = `"full"`
	)

◆ parameter() [3/3]

MX casadi::Opti::parameter	(	const Sparsity &	sp,
		const std::string &	attribute = `"full"`
	)

◆ return_status()

std::string casadi::Opti::return_status ( ) const

   passed as-is from nlpsol

No stability can be guaranteed about this part of the API

Extra doc: https://github.com/casadi/casadi/wiki/L_1g

◆ scale_helper()

Function casadi::Opti::scale_helper ( const Function & h ) const

Extra doc: https://github.com/casadi/casadi/wiki/L_2ci

◆ set_domain()

void casadi::Opti::set_domain	(	const MX &	x,
		const std::string &	domain
	)

Parameters

[in]	x	decision variable
[in]	type	'real', 'integer' (default: real)

* opti.set_domain(x, "real")
* opti.set_domain(x, "integer")
*

Extra doc: https://github.com/casadi/casadi/wiki/L_27t

◆ set_initial() [1/2]

void casadi::Opti::set_initial	(	const MX &	x,
		const DM &	v
	)

Set initial guess for decision variables

* opti.set_initial(x, 2)
* opti.set_initial(10*x(1), 2)
*

◆ set_initial() [2/2]

void casadi::Opti::set_initial ( const std::vector< MX > & assignments )

Set initial guess for decision variables

* opti.set_initial(x, 2)
* opti.set_initial(10*x(1), 2)
*

◆ set_linear_scale()

void casadi::Opti::set_linear_scale	(	const MX &	x,
		const DM &	scale,
		const DM &	offset = `0`
	)

(x-offset)/scale will be used in the optimization problem

Parameters

[in]	x	decision variable
[in]	scale	scaling value (default: 1)
[in]	offset	scaling value (default: 0)

* opti.set_linear_scale(x, 20)
* opti.set_linear_scale(x, 20, 273.15)
*

Extra doc: https://github.com/casadi/casadi/wiki/L_2bs

◆ set_value() [1/2]

void casadi::Opti::set_value	(	const MX &	x,
		const DM &	v
	)

Each parameter must be given a value before 'solve' can be called

Extra doc: https://github.com/casadi/casadi/wiki/L_1d

◆ set_value() [2/2]

void casadi::Opti::set_value ( const std::vector< MX > & assignments )

Each parameter must be given a value before 'solve' can be called

Extra doc: https://github.com/casadi/casadi/wiki/L_1d

◆ solve()

OptiSol casadi::Opti::solve ( )

◆ solve_limited()

OptiSol casadi::Opti::solve_limited ( )

Allows the solver to return without error when an iteration or time limit is reached

Extra doc: https://github.com/casadi/casadi/wiki/L_1e

◆ solver()

void casadi::Opti::solver	(	const std::string &	solver,
		const Dict &	plugin_options = `Dict()`,
		const Dict &	solver_options = `Dict()`
	)

Parameters

[in]	solver	any of the nlpsol plugins can be used here In practice, not all nlpsol plugins may be supported yet
[in]	options	passed on to nlpsol plugin No stability can be guaranteed about this part of the API
[in]	options	to be passed to nlpsol solver No stability can be guaranteed about this part of the API

Extra doc: https://github.com/casadi/casadi/wiki/L_1c

◆ stats()

Dict casadi::Opti::stats ( ) const

nlpsol stats are passed as-is. No stability can be guaranteed about this part of the API

Extra doc: https://github.com/casadi/casadi/wiki/L_1f

◆ subject_to() [1/5]

void casadi::Opti::subject_to ( )

◆ subject_to() [2/5]

void casadi::Opti::subject_to	(	const MX &	g,
		const Dict &	options = `Dict()`
	)

Examples:

* \begin{itemize}
* opti.subject_to( sqrt(x+y) >= 1);
* opti.subject_to( sqrt(x+y) > 1)}: same as above
* opti.subject_to( 1<= sqrt(x+y) )}: same as above
* opti.subject_to( 5*x+y==1 )}: equality
*
* Python
* opti.subject_to([x*y>=1,x==3])
* opti.subject_to(opti.bounded(0,x,1))
*
* MATLAB
* opti.subject_to({x*y>=1,x==3})
* opti.subject_to( 0<=x<=1 )
*

Related functionalities:

opti.lbg,opti.g,opti.ubg represent the vector of flattened constraints
opti.debug.show_infeasibilities() may be used to inspect which constraints are violated

Extra doc: https://github.com/casadi/casadi/wiki/L_1b

◆ subject_to() [3/5]

void casadi::Opti::subject_to	(	const MX &	g,
		const DM &	linear_scale,
		const Dict &	options = `Dict()`
	)

Examples:

* \begin{itemize}
* opti.subject_to( sqrt(x+y) >= 1);
* opti.subject_to( sqrt(x+y) > 1)}: same as above
* opti.subject_to( 1<= sqrt(x+y) )}: same as above
* opti.subject_to( 5*x+y==1 )}: equality
*
* Python
* opti.subject_to([x*y>=1,x==3])
* opti.subject_to(opti.bounded(0,x,1))
*
* MATLAB
* opti.subject_to({x*y>=1,x==3})
* opti.subject_to( 0<=x<=1 )
*

Related functionalities:

opti.lbg,opti.g,opti.ubg represent the vector of flattened constraints
opti.debug.show_infeasibilities() may be used to inspect which constraints are violated

Extra doc: https://github.com/casadi/casadi/wiki/L_1b

◆ subject_to() [4/5]

void casadi::Opti::subject_to	(	const std::vector< MX > &	g,
		const Dict &	options = `Dict()`
	)

Examples:

* \begin{itemize}
* opti.subject_to( sqrt(x+y) >= 1);
* opti.subject_to( sqrt(x+y) > 1)}: same as above
* opti.subject_to( 1<= sqrt(x+y) )}: same as above
* opti.subject_to( 5*x+y==1 )}: equality
*
* Python
* opti.subject_to([x*y>=1,x==3])
* opti.subject_to(opti.bounded(0,x,1))
*
* MATLAB
* opti.subject_to({x*y>=1,x==3})
* opti.subject_to( 0<=x<=1 )
*

Related functionalities:

opti.lbg,opti.g,opti.ubg represent the vector of flattened constraints
opti.debug.show_infeasibilities() may be used to inspect which constraints are violated

Extra doc: https://github.com/casadi/casadi/wiki/L_1b

◆ subject_to() [5/5]

void casadi::Opti::subject_to	(	const std::vector< MX > &	g,
		const DM &	linear_scale,
		const Dict &	options = `Dict()`
	)

Examples:

* \begin{itemize}
* opti.subject_to( sqrt(x+y) >= 1);
* opti.subject_to( sqrt(x+y) > 1)}: same as above
* opti.subject_to( 1<= sqrt(x+y) )}: same as above
* opti.subject_to( 5*x+y==1 )}: equality
*
* Python
* opti.subject_to([x*y>=1,x==3])
* opti.subject_to(opti.bounded(0,x,1))
*
* MATLAB
* opti.subject_to({x*y>=1,x==3})
* opti.subject_to( 0<=x<=1 )
*

Related functionalities:

opti.lbg,opti.g,opti.ubg represent the vector of flattened constraints
opti.debug.show_infeasibilities() may be used to inspect which constraints are violated

Extra doc: https://github.com/casadi/casadi/wiki/L_1b

◆ to_function() [1/3]

Function casadi::Opti::to_function	(	const std::string &	name,
		const std::map< std::string, MX > &	dict,
		const std::vector< std::string > &	name_in,
		const std::vector< std::string > &	name_out,
		const Dict &	opts = `Dict()`
	)

Parameters

[in]	name	Name of the resulting CasADi Function
[in]	args	List of parameters and decision/dual variables (which can be given an initial guess) with the resulting Function
[in]	res	List of expressions that will get evaluated at the optimal solution
[in]	opts	Standard CasADi Funcion options

Extra doc: https://github.com/casadi/casadi/wiki/L_1j

◆ to_function() [2/3]

Function casadi::Opti::to_function	(	const std::string &	name,
		const std::vector< MX > &	args,
		const std::vector< MX > &	res,
		const Dict &	opts = `Dict()`
	)

Parameters

[in]	name	Name of the resulting CasADi Function
[in]	args	List of parameters and decision/dual variables (which can be given an initial guess) with the resulting Function
[in]	res	List of expressions that will get evaluated at the optimal solution
[in]	opts	Standard CasADi Funcion options

Extra doc: https://github.com/casadi/casadi/wiki/L_1j

◆ to_function() [3/3]

Function casadi::Opti::to_function	(	const std::string &	name,
		const std::vector< MX > &	args,
		const std::vector< MX > &	res,
		const std::vector< std::string > &	name_in,
		const std::vector< std::string > &	name_out,
		const Dict &	opts = `Dict()`
	)

Parameters

[in]	name	Name of the resulting CasADi Function
[in]	args	List of parameters and decision/dual variables (which can be given an initial guess) with the resulting Function
[in]	res	List of expressions that will get evaluated at the optimal solution
[in]	opts	Standard CasADi Funcion options

Extra doc: https://github.com/casadi/casadi/wiki/L_1j

◆ type_name()

std::string casadi::Opti::type_name ( ) const

inline

Definition at line 440 of file optistack.hpp.

◆ ubg()

MX casadi::Opti::ubg ( ) const

◆ update_user_dict() [1/2]

void casadi::Opti::update_user_dict	(	const MX &	m,
		const Dict &	meta
	)

Add arbitrary data in the form of a dictionary to symbols or constraints

Extra doc: https://github.com/casadi/casadi/wiki/L_1o

◆ update_user_dict() [2/2]

void casadi::Opti::update_user_dict	(	const std::vector< MX > &	m,
		const Dict &	meta
	)

◆ user_dict()

Dict casadi::Opti::user_dict ( const MX & m ) const

◆ value() [1/3]

native_DM casadi::Opti::value	(	const DM &	x,
		const std::vector< MX > &	values = `std::vector< MX >()`
	)		const

Parameters

[in]	x	decision variable
[in]	type	'real', 'integer' (default: real)

* opti.set_domain(x, "real")
* opti.set_domain(x, "integer")
*

Extra doc: https://github.com/casadi/casadi/wiki/L_27t

◆ value() [2/3]

native_DM casadi::Opti::value	(	const MX &	x,
		const std::vector< MX > &	values = `std::vector< MX >()`
	)		const

Obtain value of expression at the current value

In regular mode, teh current value is the converged solution In debug mode, the value can be non-converged

Parameters

[in] values Optional assignment expressions (e.g. x==3) to overrule the current value

◆ value() [3/3]

native_DM casadi::Opti::value	(	const SX &	x,
		const std::vector< MX > &	values = `std::vector< MX >()`
	)		const

Parameters

[in]	x	decision variable
[in]	type	'real', 'integer' (default: real)

* opti.set_domain(x, "real")
* opti.set_domain(x, "integer")
*

Extra doc: https://github.com/casadi/casadi/wiki/L_27t

◆ value_parameters()

std::vector<MX> casadi::Opti::value_parameters ( ) const

◆ value_variables()

std::vector<MX> casadi::Opti::value_variables ( ) const

Extra doc: https://github.com/casadi/casadi/wiki/L_267

◆ variable() [1/3]

MX casadi::Opti::variable	(	casadi_int	n = `1`,
		casadi_int	m = `1`,
		const std::string &	attribute = `"full"`
	)

The order of creation matters. The order will be reflected in the optimization problem. It is not required for decision variables to actualy appear in the optimization problem.

Parameters

[in]	n	number of rows (default 1)
[in]	m	number of columnss (default 1)
[in]	attribute	'full' (default) or 'symmetric'

Extra doc: https://github.com/casadi/casadi/wiki/L_18

◆ variable() [2/3]

MX casadi::Opti::variable	(	const MX &	symbol,
		const std::string &	attribute = `"full"`
	)

◆ variable() [3/3]

MX casadi::Opti::variable	(	const Sparsity &	sp,
		const std::string &	attribute = `"full"`
	)

◆ x()

MX casadi::Opti::x ( ) const

Extra doc: https://github.com/casadi/casadi/wiki/L_26b

◆ x_linear_scale()

DM casadi::Opti::x_linear_scale ( ) const

◆ x_linear_scale_offset()

DM casadi::Opti::x_linear_scale_offset ( ) const

Friends And Related Function Documentation

◆ InternalOptiCallback

friend class InternalOptiCallback

friend

Definition at line 91 of file optistack.hpp.

The documentation for this class was generated from the following file:

optistack.hpp

Detailed Description

Public Types

Public Member Functions

Static Public Member Functions

Friends

Member Typedef Documentation

◆ base_type

◆ internal_base_type

Constructor & Destructor Documentation

◆ Opti()

Member Function Documentation

◆ __hash__()

◆ advanced()

◆ bounded()

◆ callback_class() [1/2]

◆ callback_class() [2/2]

◆ class_name()

◆ copy()

◆ debug()

◆ debug_repr()

◆ disp()

◆ dual()

◆ f()

◆ f_linear_scale()

◆ g()

◆ g_linear_scale()

◆ get_str()

◆ initial()

◆ is_null()

◆ lam_g()

◆ lbg()

◆ minimize()

◆ ng()

◆ np()

◆ nx()

◆ p()

◆ parameter() [1/3]

◆ parameter() [2/3]

◆ parameter() [3/3]

◆ return_status()

◆ scale_helper()

◆ set_domain()

◆ set_initial() [1/2]

◆ set_initial() [2/2]

◆ set_linear_scale()

◆ set_value() [1/2]

◆ set_value() [2/2]

◆ solve()

◆ solve_limited()

◆ solver()

◆ stats()

◆ subject_to() [1/5]

◆ subject_to() [2/5]

◆ subject_to() [3/5]

◆ subject_to() [4/5]

◆ subject_to() [5/5]

◆ to_function() [1/3]

◆ to_function() [2/3]

◆ to_function() [3/3]

◆ type_name()

◆ ubg()

◆ update_user_dict() [1/2]

◆ update_user_dict() [2/2]

◆ user_dict()

◆ value() [1/3]

◆ value() [2/3]

◆ value() [3/3]

◆ value_parameters()

◆ value_variables()

◆ variable() [1/3]

◆ variable() [2/3]

◆ variable() [3/3]

◆ x()

◆ x_linear_scale()

◆ x_linear_scale_offset()

Friends And Related Function Documentation

◆ InternalOptiCallback

◆ hash()